Finding Expert Users in Community Question Answering Services Using Topic Models

Abstract

Community Question Answering (CQA) websites provide a rapidly growing source of information in many areas. In most CQA implementations there is little effort in directing new questions to the right group of experts. This means that experts are not provided with questions matching their expertise. In this thesis, we propose a framework for automatically routing a newly posted question to the best suited expert. The purpose of this framework is to decrease the waiting time for a personal response.

We also investigate the suitability of two statistical topic models for solving this issue and compare these methods against more traditional Information Retrieval approaches. We show that for a dataset constructed from the Stackoverflow website, these topic models outperform other methods in retrieving a set of best experts. We also show that the Segmented Topic Model gives consistently better performance compared to the Latent Dirichlet Allocation Model.